Data warehouses are traditionally designed for analytical processing rather than real-time transactional processing. However, there are techniques and technologies that can be employed to update data in a data warehouse in near real-time.
Achieving true real-time updates in a data warehouse can be challenging and may not be necessary for all use cases.
The approach taken will depend on factors such as the volume and velocity of data, latency requirements, and the specific requirements of the business.
Near Real Time Updates to Data Warehouses can result in more reporting.
Change Data Capture (CDC)
CDC is a technique used to capture changes made to source data and apply them to the data warehouse. It involves identifying and capturing the changes (inserts, updates, deletes) that occur in the source system's data and then applying those changes to the data warehouse.
This can be achieved through database triggers, log-based CDC, or using specialized CDC tools.
Change Data Capture
Event-Driven Architecture
In this approach, events generated by source systems are captured in real-time and processed to update the data warehouse.
Systems like Apache Kafka or AWS Kinesis can be used to capture and stream these events, which can then be processed and transformed to update the data warehouse.
In an event driven architecture, the flow of data is driven by external and signals.
Micro-batch Processing
Instead of processing data in real-time, micro-batch processing involves processing data in small batches at regular intervals (e.g., every few seconds or minutes).
Tools like Apache Spark or Apache Flink can be used to process these batches efficiently, updating the data warehouse incrementally.
How does micro-batch processing differ from traditional batch processing?
